Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Videos

IPADE: Iterative prototype adjustment for nearest neighbor classification.

Isaac Triguero¹, Salvador Garcia, Francisco Herrera

¹Department of Computer Science and Artificial Intelligence, University of Granada, Spain. triguero@decsai.ugr.es

IEEE Transactions on Neural Networks

|November 3, 2010

Summary

This summary is machine-generated.

Related Concept Videos

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Recurrence factors in familial papillary thyroid carcinoma.

Minerva endocrinology·2025

Same author

An overview of model uncertainty and variability in LLM-based sentiment analysis: challenges, mitigation strategies, and the role of explainability.

Frontiers in artificial intelligence·2025

Same author

Cluster analysis of blood biomarkers to identify molecular patterns in pulmonary fibrosis: assessment of a multicentre, prospective, observational cohort with independent validation.

The Lancet. Respiratory medicine·2024

Same author

SEGAL time series classification - Stable explanations using a generative model and an adaptive weighting method for LIME.

Neural networks : the official journal of the International Neural Network Society·2024

Same author

RSAFormer: A method of polyp segmentation with region self-attention transformer.

Computers in biology and medicine·2024

Same author

Hybrid Gromov-Wasserstein Embedding for Capsule Learning.

IEEE transactions on neural networks and learning systems·2024

Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013

Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011

Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011

Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011

Same journal

Data-based identification and control of nonlinear systems via piecewise affine approximation.

IEEE transactions on neural networks·2011

Same journal

Data-core-based fuzzy min-max neural network for pattern classification.

IEEE transactions on neural networks·2011

See all related articles

Nearest prototype methods in pattern classification can be improved. This study introduces an iterative prototype adjustment technique using differential evolution, enhancing nearest neighbor classifier performance and addressing limitations like speed and noise sensitivity.

Area of Science:

Computer Science
Machine Learning
Pattern Recognition

Background:

Nearest prototype methods are widely used in pattern classification.
These methods face challenges including slow response times, sensitivity to noise, and high storage demands.
Data reduction techniques, particularly prototype generation, can mitigate these drawbacks.

Purpose of the Study:

To present a novel methodology for iteratively learning prototype positions.
To address the limitations of traditional nearest prototype methods.
To enhance the performance of nearest neighbor (NN) classifiers.

Main Methods:

An iterative prototype adjustment technique based on differential evolution is proposed.
This method optimizes prototype positioning for improved data fitting.

Related Experiment Videos

The approach focuses on real parameter optimization procedures.

Main Results:

The proposed technique consistently outperforms existing methods.
Nonparametric statistical tests validate the superior performance.
The method effectively enhances the performance of the NN classifier.

Conclusions:

The iterative prototype adjustment technique is a valuable tool for improving NN classifiers.
This method offers a robust solution for data reduction and prototype generation.
The approach successfully addresses the shortcomings of traditional nearest prototype methods.